Articles | Volume 14, issue 12
https://doi.org/10.5194/amt-14-7435-2021
https://doi.org/10.5194/amt-14-7435-2021
Research article
 | 
30 Nov 2021
Research article |  | 30 Nov 2021

Support vector machine tropical wind speed retrieval in the presence of rain for Ku-band wind scatterometry

Xingou Xu and Ad Stoffelen

Viewed

Total article views: 2,038 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,523 464 51 2,038 27 32
  • HTML: 1,523
  • PDF: 464
  • XML: 51
  • Total: 2,038
  • BibTeX: 27
  • EndNote: 32
Views and downloads (calculated since 26 Jul 2021)
Cumulative views and downloads (calculated since 26 Jul 2021)

Viewed (geographical distribution)

Total article views: 2,038 (including HTML, PDF, and XML) Thereof 1,926 with geography defined and 112 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 22 Feb 2024
Download
Short summary
The support vector machine can effectively represent the increasing effect of rain affecting wind speeds. This research provides a correction of deviations that are skew- to Gaussian-like features caused by rain in Ku-band scatterometer wind. It demonstrates the effectiveness of a machine learning method when used based on elaborate analysis of the model establishment and result validation procedures. The corrected winds provide information previously lacking, which is vital for nowcasting.